基于无导数非线性卡尔曼滤波的欠驱动气垫船非线性控制

G. Rigatos, G. Raffo
{"title":"基于无导数非线性卡尔曼滤波的欠驱动气垫船非线性控制","authors":"G. Rigatos, G. Raffo","doi":"10.1109/UKCI.2014.6930154","DOIUrl":null,"url":null,"abstract":"The paper proposes a nonlinear control approach for the underactuated hovercraft model based on differential flatness theory and using a new nonlinear state vector and disturbances estimation method under the name of Derivative-free nonlinear Kalman Filter. It is proven that the sixth order nonlinear model of the hovercraft is a differentially flat one. It is shown that this model cannot be subjected to static feedback linearization, however it admits dynamic feedback linearization which means that the system's state vector is extended by including as additional state variables the control inputs and their derivatives. Next, using the differential flatness properties it is also proven that this model can be subjected to input-output linearization and can be transformed to an equivalent canonical (Brunovsky) form. Based on this latter description the design of a state feedback controller is carried out enabling accurate maneuvering and trajectory tracking. Additional problems that are solved in the design of this feedback control scheme are the estimation of the nonmeasurable state variables in the hovercraft's model and the compensation of modeling uncertainties and external perturbations affecting vessel. To this end, the application of the Derivative-free nonlinear Kalman Filter is proposed. This nonlinear filter consists of the Kalman Filter's recursion on the linearized equivalent of the vessel and of an inverse nonlinear transformation based on the differential flatness features of the system which enables to compute state estimates for the state variables of the initial nonlinear model. The redesign of the filter as a disturbance observer makes possible the estimation and compensation of additive perturbation terms affecting the hovercraft's model. The efficiency of the proposed nonlinear control and state estimation scheme is confirmed through simulation experiments.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Nonlinear control of the underactuated hovercraft using the Derivative-free nonlinear Kalman filter\",\"authors\":\"G. Rigatos, G. Raffo\",\"doi\":\"10.1109/UKCI.2014.6930154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposes a nonlinear control approach for the underactuated hovercraft model based on differential flatness theory and using a new nonlinear state vector and disturbances estimation method under the name of Derivative-free nonlinear Kalman Filter. It is proven that the sixth order nonlinear model of the hovercraft is a differentially flat one. It is shown that this model cannot be subjected to static feedback linearization, however it admits dynamic feedback linearization which means that the system's state vector is extended by including as additional state variables the control inputs and their derivatives. Next, using the differential flatness properties it is also proven that this model can be subjected to input-output linearization and can be transformed to an equivalent canonical (Brunovsky) form. Based on this latter description the design of a state feedback controller is carried out enabling accurate maneuvering and trajectory tracking. Additional problems that are solved in the design of this feedback control scheme are the estimation of the nonmeasurable state variables in the hovercraft's model and the compensation of modeling uncertainties and external perturbations affecting vessel. To this end, the application of the Derivative-free nonlinear Kalman Filter is proposed. This nonlinear filter consists of the Kalman Filter's recursion on the linearized equivalent of the vessel and of an inverse nonlinear transformation based on the differential flatness features of the system which enables to compute state estimates for the state variables of the initial nonlinear model. The redesign of the filter as a disturbance observer makes possible the estimation and compensation of additive perturbation terms affecting the hovercraft's model. The efficiency of the proposed nonlinear control and state estimation scheme is confirmed through simulation experiments.\",\"PeriodicalId\":315044,\"journal\":{\"name\":\"2014 14th UK Workshop on Computational Intelligence (UKCI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 14th UK Workshop on Computational Intelligence (UKCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UKCI.2014.6930154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 14th UK Workshop on Computational Intelligence (UKCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKCI.2014.6930154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本文提出了一种基于微分平坦度理论的欠驱动气垫船模型的非线性控制方法,采用一种新的非线性状态向量和扰动估计方法——无导数非线性卡尔曼滤波。证明了气垫船的六阶非线性模型是差分平面模型。结果表明,该模型不能承受静态反馈线性化,但它允许动态反馈线性化,这意味着系统的状态向量被扩展,包括作为附加状态变量的控制输入及其导数。接下来,利用微分平坦性,还证明了该模型可以经受输入输出线性化,并且可以转换为等效的规范(布鲁诺夫斯基)形式。在此基础上,设计了一种状态反馈控制器,实现了精确的机动和轨迹跟踪。该反馈控制方案的设计还解决了气垫船模型中不可测状态变量的估计、模型不确定性和影响气垫船的外部扰动的补偿等问题。为此,提出了无导数非线性卡尔曼滤波器的应用。该非线性滤波器由卡尔曼滤波器在容器的线性化等效上的递归和基于系统的微分平坦度特征的非线性逆变换组成,该逆变换可以计算初始非线性模型的状态变量的状态估计。将滤波器重新设计为扰动观测器,使影响气垫船模型的加性扰动项的估计和补偿成为可能。仿真实验验证了所提出的非线性控制和状态估计方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Nonlinear control of the underactuated hovercraft using the Derivative-free nonlinear Kalman filter
The paper proposes a nonlinear control approach for the underactuated hovercraft model based on differential flatness theory and using a new nonlinear state vector and disturbances estimation method under the name of Derivative-free nonlinear Kalman Filter. It is proven that the sixth order nonlinear model of the hovercraft is a differentially flat one. It is shown that this model cannot be subjected to static feedback linearization, however it admits dynamic feedback linearization which means that the system's state vector is extended by including as additional state variables the control inputs and their derivatives. Next, using the differential flatness properties it is also proven that this model can be subjected to input-output linearization and can be transformed to an equivalent canonical (Brunovsky) form. Based on this latter description the design of a state feedback controller is carried out enabling accurate maneuvering and trajectory tracking. Additional problems that are solved in the design of this feedback control scheme are the estimation of the nonmeasurable state variables in the hovercraft's model and the compensation of modeling uncertainties and external perturbations affecting vessel. To this end, the application of the Derivative-free nonlinear Kalman Filter is proposed. This nonlinear filter consists of the Kalman Filter's recursion on the linearized equivalent of the vessel and of an inverse nonlinear transformation based on the differential flatness features of the system which enables to compute state estimates for the state variables of the initial nonlinear model. The redesign of the filter as a disturbance observer makes possible the estimation and compensation of additive perturbation terms affecting the hovercraft's model. The efficiency of the proposed nonlinear control and state estimation scheme is confirmed through simulation experiments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
PermGA algorithm for a sequential optimal space filling DoE framework Modeling neural plasticity in echo state networks for time series prediction Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation Adaptive mutation in dynamic environments Automatic image annotation with long distance spatial-context
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1